Multi-product exporters, carry-along trade and the margins of trade
Bibliographic record
Abstract
New empirical and theoretical work has highlighted the importance of multi-product firms in international tradeflows. We examine multi-product exporters in the small open economy of Belgium, considering their importance and the relationship between the margins of trade and firm productivity, both across firms and within firms over time. In addition, we employ proxies for trade costs to quantify the extensive and intensive margin adjustments of trade. Linking production and export data at the firm-product level, we discover new and, heretofore, unknown facts about multi-product manufacturing exporters. The large majority of Belgian manufacturing firms export products that they do not produce. More than three quarters of the exported products and more than one quarter of export value from Belgian manufacturers are in goods that are not produced by the firm, so-called Carry-Along Trade (CAT). CAT exports are concentrated in the largest and most productive firms and the value of CAT exports responds differently to variation in firm productivity and trade costs than does the export value of goods that the firm produces.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".